Since the introduction of advanced mobile devices with data-intensive applications, cellular networks are witnessing rapidly increasing data traffic demands from mobile users. To keep up with the increasing traffic demands, cellular networks are being transformed into heterogeneous networks (HetNets) by the deployment of small cells (e.g., picocell, femtocell, etc.) over the existing macrocells. Cisco has recently predicted an 11-fold increase in global mobile data traffic between 2013 and 2018 [1], while Qualcomm has predicted an astounding 1000× increase in mobile data traffic [2]. Addressing this challenge will lead to extreme densification of the small cells, which will give rise to hyper-dense heterogeneous networks (HDHNs).
Mobility management in cellular networks is an important aspect of providing good quality of service to mobile users by minimizing handover failures. In homogeneous networks that only have macrocell base stations (MBSs), handovers are typically finalized at the cell edge due to the large cell sizes. With the deployment of small cell base stations (SBSs), it becomes more difficult to finalize the handover process at the cell edge for the device or user equipment (UE) due to the smaller cell size [3], [4].
In particular, high-mobility devices may run deep inside the coverage areas of small cells before finalizing a handover, thus incurring handover failure because of degraded signal to interference plus noise ratio (SINR). These challenges motivate the need to set handover parameters according to accurate UE velocity estimations and target cell size.
Existing LTE and LTE-Advanced technologies are capable of estimating the mobility state of a UE into three broad classes: low, medium, and high-mobility [4]-[6]. This is achieved on the device by counting the number of handovers within a given time window and comparing it with a threshold of the device. A velocity estimate can also be implemented at the network side by tracking the prior history of handovers for a particular UE. The coarse mobility state estimate can then be used, for example, to modify handover related parameters. While more accurate UE-side speed estimation techniques based on Doppler estimation have been discussed in [9]-[12], due to their complexity and standardization challenges, they have not been adopted in existing cellular network standards.